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1.

Abrajano and Lajevardi 2021 "(Mis)Informed: What Americans Know About Social Groups and Why it Matters for Politics" reported (p. 34) that:

We find that White Americans, men, the racially resentful, Republicans, and those who turn to Fox and Breitbart for news strongly predict misinformation about [socially marginalized] social groups.

But their research design is biased toward many or all of these results, given their selection of items for their 14-item set of misinformation items. I'll focus below on left/right political bias, and then discuss apparent errors in the publication.

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Item #7 is a true/false item:

Most terrorist incidents on US soil have been conducted by Muslims.

This item will code as misinformed some participants who overestimate the percentage of U.S.-based terror attacks committed by Muslims, but won't code as misinformed any participants who underestimate that percentage.

It seems reasonable to me that persons on the political Left will be more likely than persons on the Right to underestimate the percentage of U.S.-based terror attacks committed by Muslims and that persons on the political Right will be more likely than persons on the Left to overestimate the percentage of U.S.-based terror attacks committed by Muslims, so I'll code this item as favoring the political Left.

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Four items (#11 to #14) ask about Black/White differences in receipt of federal assistance, but phrased so that Whites are the "primary recipients" of food stamps, welfare, and social security.

But none of these items measured misinformation about receipt of federal assistance as a percentage. So participants who report that the *number* of Blacks who receive food stamps is higher than the number of Whites who receive food stamps get coded as misinformed. But participants who mistakenly think that the *percentage* of Whites who receive food stamps is higher than the percentage of Blacks who receive food stamps do not get coded as misinformed.

Table 2 of this U.S. government report indicates that, in 2018, non-Hispanic Whites were 67% of households, 45% of households receiving SNAP (food stamps), and 70% of households not receiving SNAP. Respective percentages for Blacks were 12%, 27%, and 11% and for Hispanics were 13.5%, 22%, and 12%. So, based on this, it's correct that Whites are the largest racial/ethnic group that receives food stamps on a total population basis...but it's also true that Whites are the largest racial/ethnic group that does NOT receive food stamps on a total population basis.

It seems reasonable to me that the omission of percentage versions of these three public assistance items favors the political Left, in the sense that persons on the political Left are more likely to rate Blacks higher than Whites than are persons on the political Right, or, for that matter, Independents and moderates, so that these persons on the Left would presumably be more likely than persons on the Right to prefer (and thus guess) that Whites and not Blacks are the primary recipients of federal assistance. So, by my count, that's at least four items that favor the political Left.

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As far as I can tell, Abrajano and Lajevardi 2021 didn't provide citations to justify their coding of correct responses. But it seems to me that such citation should be a basic requirement for research that codes responses as correct, except for  obvious items such as, say, who the current Vice President is. A potential problem with this lack of citation is that it's not clear to me that some responses that Abrajano and Lajevardi 2021 coded as correct are truly correct or at least are the only responses that should be coded as correct.

Abrajano and Lajevardi 2021 coded "Whites" as the only correct response for the "primary recipients" item about welfare, but this government document indicates that, for 2018, the distribution of TANF recipients was 37.8% Hispanic, 28.9% Black, 27.2% White, 2.1% multi-racial, 1.9% Asian, 1.5% AIAN, and 0.6% NHOPI.

And "about the same" is coded as the only correct response for the item about the "primary recipients" of public housing (item #14), but Table 14 of this CRS Report indicates that, in 2017, 33% of public housing had a non-Hispanic White head of household and 43% had a non-Hispanic Black head of household. This webpage permits searching for "public housing" for different years (screenshot below), which, for 2016, indicates percentages of 45% for non-Hispanic Blacks and 29% for non-Hispanic Whites.

Moreover, it seems suboptimal to have the imprecise "about the same" response be the only correct response. Unless outcomes for Blacks and Whites are exactly the same, presumably selection of one or the other group should count as the correct response.

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Does a political bias in the Abrajano and Lajevardi 2021 research design matter? I think that the misinformation rates are close enough so that it matters: Figure A2 indicates that the Republican/Democrat misinformation gap is less than a point, with misinformed means of 6.51 for Republicans and 5.83 for Democrats.

Ironically, Abrajano and Lajevardi 2021 Table A1 indicates that their sample was 52% Democrat and 21% Republican, so -- on the "total" basis that Abrajano and Lajevardi 2021 used for the federal assistance items -- Democrats were the "primary" partisan source of misinformation about socially marginalized groups.

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NOTES

1. Abrajano and Lajevardi 2021 (pp. 24-25) refers to a figure that isn't in the main text, and I'm not sure where it is:

When we compare the misinformation rates across the five social groups, a number of notable patterns emerge (see Figure 2)...At the same time, we recognize that the magnitude of difference between White and Asian American's [sic] average level of misinformation (3.4) is not considerably larger than it is for Blacks (3.2), nor for Muslim American respondents, who report the lowest levels of misinformation.

Table A5 in the appendix indicates that Blacks had a lower misinformation mean than Muslims did, 5.583 compared to 5.914, so I'm not sure what the aforementioned passage refers to. The passage phrasing refers to a "magnitude of difference", but 3.4 doesn't seem to refer to a social group gap or to an absolute score for any of the social groups.

2. Abrajano and Lajevardi 2021 footnote 13 is:

Recall that question #11 is actually four separate questions, which brings us to a total of thirteen questions that comprise this aggregate measure of political misinformation.

Question 11 being four separate questions means that there are 14 questions, and Abrajano and Lajevardi 2021 refers to "fourteen" questions elsewhere (pp. 6, 17).

Abrajano and Lajevardi 2021 indicated that "...we also observe about 11% of individuals who provided inaccurate answers to all or nearly all of the information questions" (p. 24, emphasis in the original), and it seems a bit misleading to italicize "all" if no one provided inaccurate responses to all 14 items.

3. Below, I'll discuss the full set of 14 "misinformation" items. Feel free to disagree with my count, but I would be interested in an argument that the 14 items do not on net bias results toward the Abrajano and Lajevardi 2021 claim that Republicans are more misinformed than Democrats about socially marginalized groups.

For the aforementioned items, I'm coding items #7 (Muslim terror %), #11 (food stamps), #12 (welfare), and #14 (public housing) as biased in favor of the political Left, because I think that these items are phrased so that the items will catch more misinformation among the political Right than among the political Left, even though the items could be phrased to catch more misinformation among the Left than among the Right.

I'm not sure about the item about social security (#13) , so I won't code that item as politically biased. So by my count that's 4 in favor of the Left, plus 1 neutral.

Item #5 seems to be a good item, measuring whether participants know that Blacks and Latinos are more likely to live in regions with environmental problems. But it's worth noting that this item is phrased in terms of rates and not, as for the federal assistance items, as the total number of persons by racial/ethnic group. So by my count that's 4 in favor of the Left, plus 2 neutral.

Item #1 is about the number of undocumented immigrants in the United States. I won't code that item as politically biased. So by my count that's 4 in favor of the Left, plus 3 neutral.

The correct response for item #2 is that most immigrants in the United States are here legally. I'll code this item as favoring the political Left for the same reason as the Muslim terror % item: the item catches participants who overestimate the percentage of immigrants here illegally, but the item doesn't catch participants who underestimate that percentage, and I think these errors are more likely on the Right and Left, respectively. So by my count that's 5 in favor of the Left, plus 3 neutral.

Item #6 is about whether *all* (my emphasis) U.S. universities are legally permitted to consider race in admissions. It's not clear to me why it's more important that this item be about *all* U.S. universities instead of about *some* or *most* U.S. universities. I think that it's reasonable to suspect that persons on the political Right will overestimate the prevalence of affirmative action and that persons on the political Left will underestimate the prevalence of affirmative action, so by my count that's 6 in favor of the Left, plus 3 neutral.

I'm not sure that items #9 and #10 have much of a bias (number of Muslims in the United States, and the country that has the largest number of Muslims), other than to potentially favor Muslims, given that the items measure knowledge of neutral facts about Muslims. So by my count that's 6 in favor of the Left, plus 5 neutral.

I'm not sure what "social group" item #8 is supposed to be about, which is about whether Barack Obama was born in the United States. I'm guessing that a good percentage of "misinformed" responses for this item are insincere. Even if it were a good idea to measure insincere responses to test a hypothesis about misinformation, I'm not sure why it would be a good idea to not also include a corresponding item about a false claim that, like the Obama item, is known to be more likely to be accepted among the political Left, such as items about race and killings by police. So I'll up the count to 7 in favor of the Left, plus 5 neutral.

Item #4 might reasonably be described as favoring the political Right, in the sense that I think that persons on the Right would be more likely to prefer that Whites have a lower imprisonment rate than Blacks and Hispanics. But the item has this unusual element of precision ("six times", "more than twice") that isn't present in items about hazardous waste and about federal assistance, so that, even if persons on the Right stereotypically guess correctly that Blacks and Hispanics have higher imprisonment rates than Whites, these persons still might not be sure that the "six times" and "more than twice" are correct.

So even though I think that this item (#4) can reasonably be described as favoring the political Right, I'm not sure that it's as easy for the Right to use political preferences to correctly guess this item as it is for the Left to use political preferences to correctly guess the hazardous waste item and the federal assistance items. But I'll count this item as favoring the Right, so by my count that's 7 in favor of the Left, 1 in favor of the Right, plus 5 neutral.

Item #3 is about whether the U.S. Census Bureau projects ethnic and racial minorities to be a majority in the United States by 2042. I think that it's reasonable that a higher percentage of persons on the political Left than the political Right would prefer this projection to be true, but maybe fear that the projection is true might bias this item in favor of the Right. So let's be conservative and count this item as favoring the Right, so that my coding of the overall distribution for the 14 misinformation items is: seven items favoring the Left, two items favoring the Right, and five politically neutral items.

4. The ANES 2020 Time Series Study has similar biases in its set of misinformation items.

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Meta-Psychology has published my manuscript "Perceived Discrimination against Black Americans and White Americans".

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Norton and Sommers 2011 presented evidence for a claim that has become widely cited, that "Whites have now come to view anti-White bias as a bigger societal problem than anti-Black bias". I was skeptical of that claim, so I checked the data for the American National Election Studies 2012 Time Series Study, which was the most recent ANES Time Series Study available at the time. These ANES data contradicted that claim.

I preregistered an analysis of the then-upcoming ANES 2016 Time Series Study and then preregistered an analysis of an item on a 2017 survey that YouGov conducted for me with different wording for the measure of perceived discrimination.

Each of these three sets of data indicated that a higher percentage of Whites reported the perception that there is more discrimination in the United States today against Blacks than against Whites, compared to the percentage of Whites that reported the perception that there is more discrimination in the United States today against Whites than against Blacks. And it's not particularly close. Here is a new data point: in weighted analyses of data from the ANES 2020 Time Series Study, 63% of non-Hispanic Whites rated discrimination against Blacks as larger than discrimination against Whites, but only 8% of non-Hispanic Whites rated discrimination against Whites as larger than discrimination against Blacks.

The Meta-Psychology article has an explanation for why the Norton and Sommers 2011 claim appears to be incorrect.

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NOTE

1. Data source: American National Election Studies. 2021. ANES 2020 Time Series Study Preliminary Release: Combined Pre-Election and Post-Election Data [dataset and documentation]. March 24, 2021 version. www.electionstudies.org.

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Racial resentment (also known as symbolic racism) is a common measure of racial attitudes in social science. See this post for items commonly used for racial resentment measures. For this post, I'll report plots about racial resentment, using data from the American National Election Studies 2020 Time Series Study.

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This first plot reports the percentage of respondents that rated Whites, Blacks, Hispanics, and Asians/Asian-Americans equally on 0-to-100 feeling thermometers, at each level of a 0-to-16 racial resentment index. Respondents at the lowest level of racial resentment had a lower chance of rating the included racial groups equally, compared to respondents at moderate levels of racial resentment or even compared to respondents at the highest level of racial resentment.

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This next plot reports the mean racial resentment for various groups. The top section is based on responses to 0-to-100 feeling thermometers about Whites, Blacks, Hispanics, and Asians/Asian-Americans. Respondents who rated all four included racial groups equally fell at about the middle of the racial resentment index, and respondents who reported isolated negative ratings about Whites (i.e., rated Whites under 50 but rated Blacks, Hispanics, and Asian/Asian-Americans at 50 or above) fell toward the low end of the racial resentment index.

The bottom two sections of the above plot report mean racial resentment based on responses to the "lazy" and "violent" stereotype items.

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So I think that the above plots indicate that low levels of racial resentment aren't obviously normatively good.

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Below is an update on how well racial resentment predicts attitudes about the environment and some other things that I don't expect to have a strong direct causal relationship with racial attitudes. The plot below reports OLS regression coefficients for racial resentment on a 0-to-1 scale, predicting the indicated outcomes on a 0-to-1 scale, with controls for gender, age group, education, marital status, income, partisanship, and ideology, all controlled for using categorical predictors.

The estimated effects of racial resentment on attitudes about federal spending on welfare and federal spending on crime (attitudes presumably related to race) are of similar "small to moderately small" size as the estimated effects of racial resentment on attitudes about greenhouse regulations, climate change causing severe weather, and federal spending on the environment.

Racial resentment had the ability to predict attitudes about the environment net of controls in ANES data from 1986.

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NOTES

1. Data source: American National Election Studies. 2021. ANES 2020 Time Series Study Preliminary Release: Combined Pre-Election and Post-Election Data [dataset and documentation]. March 24, 2021 version. www.electionstudies.org.

2. Stata and R code. Dataset for Plot 1. Dataset for Plot 2. Dataset for Plot 3.

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Social Science Quarterly recently published Cooper et al. 2021 "Heritage Versus Hate: Assessing Opinions in the Debate over Confederate Monuments and Memorials". The conclusion of the article notes that:

...we uncover significant evidence that the debate over Confederate monuments can be resoundingly summarized as "hate" over "heritage"

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In a prior post, I noted that:

...when comparing the estimated effect of predictors, inferences can depend on how well each predictor is measured, so such analyses should discuss the quality of the predictors.

Cooper et al. 2021 measured "heritage" with a dichotomous predictor and measured "hate" with a five-level predictor, and this difference in the precision of the measurements could have biased their research design toward a larger estimate for hate than for heritage. [See note 3 below for a discussion].

I'm not suggesting that the entire difference between their estimates for heritage and hate is due to the number of levels of the predictors, but I think that a better peer review would have helped eliminate that flaw in the research design, maybe by requiring the measure of hate to be dichotomized as close as possible to 70/30 like the measure of heritage was.

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Here is the lone measure of heritage used in Cooper et al. 2021:

"Do you consider yourself a Southerner, or not?"

Table 1 of the article indicates that 70% identified as a Southerner, so even if this were a face-valid measure of Southern heritage, the measure places into the highest level of Southern heritage persons at the 35th percentile of Southern heritage.

Maybe there is more recent data that undercuts this, but data from the Spring 2001 Southern Focus Poll indicated that only about 1 in 3 respondents who identified as a Southerner indicated that being a Southerner was "very important" to them. About 1 in 3 respondents who identified as a Southerner in that 2001 poll indicated that being a Southerner was "not at all important" or "not very important" to them, and I can't think of a good reason why, without other evidence, these participants belong in the highest level of a measure of Southern heritage.

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Wright and Esses 2017 had a more precise measure for heritage and found sufficient evidence to conclude that (p. 232):

Positive attitudes toward the Confederate battle flag were more strongly associated with Southern pride than with racial attitudes when accounting for these covariates.

How does Cooper et al. 2021 address the Wright and Esses 2017 result, which conflicts with the result from Cooper et al. 2021 and which used a related outcome variable and a better measure of heritage? The Cooper et al. 2021 article doesn't even mention Wright and Esses 2017.

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A better peer review might have caught the minimum age of zero years old in Table 1 and objected to the description of "White people are currently under attack in this country" as operationalizing "racial resentment toward blacks" (pp. 8-9), given that this item doesn't even mention or refer to Blacks. I suppose that respondents who hate White people would be reluctant to agree that White people are under attack regardless of whether that is true. But that's not the "hate" that is supposed to be measured.

Estimating the effect of "hate" for this type of research should involve comparing estimates net of controls for respondents who have a high degree of hate for Blacks to respondents who are indifferent to Blacks. Such estimates can be biased if the estimates instead include data from respondents who have more negative feelings about Whites than about Blacks. In a prior post, I discussed Carrington and Strother 2020, which measured hate with a Black/White feeling thermometer difference and thus permitted estimation of how much of the effect of hate is due to respondents rating Blacks higher than Whites on the feeling thermometers.

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Did Cooper et al. have access to better measures of hate than the item "White people are currently under attack in this country"? The Winthrop Poll site didn't list the Nov 2017 survey on its archived poll page for 2017. But, from what I can tell, this Winthrop University post discusses the survey, which included a better measure of racial resentment toward blacks. I don't know what information the peer reviewers of Cooper et al. 2021 had access to, but, generally, a journal reform that I would like to see for manuscripts reporting on a survey is for peer reviewers to be given access to the entire set of items for a survey.

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In conclusion, for a study that compares the estimated effects of heritage and hate, I think that at least three things are needed: a good measure of heritage, a good measure of hate, and the good measure of heritage being of similar quality to the good measure of hate. I don't think that Cooper et al. 2021 has any of those things.

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NOTES

1. The Spring 2001 Southern Focus Poll study was conducted by the Odum Institute for Research in Social Science of the University of North Carolina at Chapel Hill. Citation: Center for the Study of the American South, 2001, "Southern Focus Poll, Spring 2001", https://hdl.handle.net/1902.29/D-31552, UNC Dataverse, V1.

2. Stata output.

3. Suppose that mean support for leaving Confederate monuments as they are were 70% among the top 20 percent of respondents by Southern pride, 60% among the next 20 percent of respondents by Southern pride, 50% among the middle 20 percent, 40% among the next 20 percent, and 30% among the bottom 20 percent of respondents by Southern pride. And let's assume that these bottom 20 percent are indifferent about Southern pride and don't hate Southerners.

The effect of Southern pride could be estimated at 40 percentage points, which is the difference in support among the top 20 percent and bottom 20 percent by Southern pride. However, if we grouped the top 60 percent together and the bottom 40 percent together, the mean percentage support would respectively be 60% and 35%, for an estimated effect of 25 percentage points. In this illustration, the estimated effect for the five-level predictor is larger than the estimate for the dichotomous predictor, even with the same data.

Here is a visual illustration:

The above is a hypothetical to illustrate the potential bias in measuring one predictor with five levels and another predictor with two levels. I have no idea whether this had any effect on the results reported in Cooper et al. 2021. But, with a better peer review, readers would not need to worry about this type of bias in the Cooper et al. 2021 research design.

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The plot below reports the mean rating from Whites, Blacks, Hispanics, and Asians of Whites, Blacks, Hispanics, and Asians, using data from the preliminary release of the 2020 ANES Time Series Study.

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NOTES

1. Data source: American National Election Studies. 2021. ANES 2020 Time Series Study Preliminary Release: Combined Pre-Election and Post-Election Data [dataset and documentation]. March 24, 2021 version. www.electionstudies.org.

2. Stata code. Stata output. R code for the plots. Dataset for the R plot.

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The journal Politics, Groups, and Identities recently published Mangum and Block Jr. 2021 "Perceived racial discrimination, racial resentment, and support for affirmative action and preferential hiring and promotion: a multi-racial analysis".

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The article notes that (p. 13):

Intriguingly, blame [of racial and ethnic minorities] tends to be positively associated with support for preferential hiring and promotion, and, in 2008, this positive relationship is statistically significant for Black and Asian respondents (Table A4; lower right graph in Figure 6). This finding is confounding...

But from what I can tell, this finding might be because the preferential hiring and promotion outcome variable was coded backwards to the intended coding. Table 2 of the article indicates that a higher percentage of Blacks than of Whites, Hispanics, and Asians favored preferential hiring and promotion, but Figures 1 and 2 indicate that a lower percentage of Blacks than of Whites, Hispanics, and Asians favored preferential hiring and promotion.

My analysis of data for the 2004 National Politics Study indicated that the preferential hiring and promotion results in Table 2 are correct for this survey and that blame of racial and ethnic minorities negatively associates with favoring preferential hiring and promotion.

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Other apparent errors in the article include:

Page 4:

Borrowing from the literature on racial resentment possessed (Feldman and Huddy 2005; Kinder and Sanders 1996; Kinder and Sears 1981)...

Figures 3, 4, 5, and 6:

...holding control variable constant

Page 15:

African Americans, Hispanics, and Asians support affirmative action more than are Whites.

Page 15:

Preferential hiring and promotion is about who deserves special treatment than affirmative action, which is based more on who needs it to overcome discrimination.

Note 2:

...we code the control variables to that they fit a 0-1 scale...

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Moreover, the article indicates that "the Supreme Court ruled that affirmative action was constitutional in California v. Bakke in 1979", which is not the correct year. And the article seems to make inconsistent claims about affirmative action: "affirmative action and preferential hiring and promotion do not benefit Whites" (p. 15), but "White women are the largest beneficiary group (Crosby et al. 2003)" (p. 13).

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At least some of these flaws seem understandable. But I think that the number of flaws in this article is remarkably high, especially for a peer-reviewed journal with such a large editorial group: Politics, Groups, and Identities currently lists a 13-member editorial team, a 58-member editorial board, and a 9-member international advisory board.

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NOTES

1. The article claims that (p. 15):

Regarding all races, most of the racial resentment indicators are significant statistically and in the hypothesized direction. These findings lead to the conclusion that preferential hiring and promotion foster racial thinking more than affirmative action. That is, discussions of preferential hiring and promotion lead Americans to consider their beliefs about minorities in general and African Americans in particular more than do discussions of affirmative action.

However, I'm not sure of how the claim that "preferential hiring and promotion foster racial thinking more than affirmative action" is justified by the article's results regarding racial resentment.

Maybe this refers to the slopes being steeper for the preferential hiring and promotion outcome than for the affirmative action outcome, but it would be a lot easier to eyeball slopes across figures if the y-axes were consistent across figures; instead, the y-axes run from .4 to .9 (Figure 3), .4 to 1 (Figure 4), .6 to 1 (Figure 5), and .2 to 1 (Figure 6).

Moreover, Figure 1 is a barplot that has a y-axis that runs from .4 to .8, and Figure 2 is a barplot that has a y-axis that runs from .5 to .9, with neither barplot starting at zero. It might make sense for journals to have an editorial board member or other person devoted to reviewing figures, to eliminate errors and improve presentation.

For example, the article indicates that (p. 6):

Figures 1 and 2 display the distribution of responses for our re-coded versions of the dependent variables graphically, using bar graphs containing 95% confidence intervals. To interpret these graphs, readers simply check to see if the confidence intervals corresponding to any given bar overlap with those of another.

But if the intent is to use confidence interval overlap to assess whether there is sufficient evidence at p<0.05 of a difference between groups, then confidence intervals closer to 85% are more appropriate. I haven't always known this, but this does seem to be knowledge that journal editors should use to foster better figures.

2. Data citation:

James S. Jackson, Vincent L. Hutchings, Ronald Brown, and Cara Wong. National Politics Study, 2004. ICPSR24483-v1. Ann Arbor, MI: Bibliographic Citation: Inter-university Consortium for Political and Social Research [distributor], 2009-03-23. doi:10.3886/ICPSR24483.v1.

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[UPDATE] The color scheme for the first two plots has been changed, based on a comment from John, below. Original plots had the red and blue reversed [1, 2].

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Below are plots of 0-to-100 feeling thermometer responses from the 2020 ANES Social Media Study.

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The first plot indicates that, compared to Blacks in the oldest age category, a higher percentage of Blacks in the youngest age category reported cold feelings (under a rating of 50) toward the four included racial groups:

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This second plot indicates that the pattern by age for Black respondents is limited to White respondents' ratings of Whites:

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I checked data in this third plot after reading the Lee and Huang 2021 post discussing recent anti-Asian violence, which indicated that:

A recent study finds that in fact, Christian nationalism is the strongest predictor of xenophobic views of COVID-19, and the effect of Christian nationalism is greater among white respondents, compared to Black respondents.

The 2020 Social Media Study didn't appear to have good items for measuring Christian nationalism, but below I used White born again Christian Trump voters as a reasonably related group. A relatively low percentage of this group rated Asians under 50, compared to the percentage of Black respondents that rated Asians under 50.

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And the fourth plot is for all White respondents compared to all Black respondents:

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NOTES

[1] Data source: American National Election Studies. 2021. ANES 2020 Social Media Study: Pre-Election Data [dataset and documentation]. March 8, 2021 version. www.electionstudies.org.

[2] Stata code for the analysis and R code for the plots. Data for plots 1, 2, 3, and 4. Stata output.

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